The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Junho Song - One of the best experts on this subject based on the ideXlab platform.
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System reliability analysis using dominant failure modes identified by selective searching technique
Reliability Engineering & System Safety, 2013Co-Authors: Dong-seok Kim, Junho Song, Hyun-moo KohAbstract:Abstract The failure of a redundant structural system is often described by innumerable system failure modes such as combinations or sequences of local failures. An efficient approach is proposed to identify dominant failure modes in the space of random variables, and then perform system reliability analysis to compute the system failure probability. To identify dominant failure modes in the decreasing order of their contributions to the system failure probability, a new simulation-based selective searching technique is developed using a genetic algorithm. The system failure probability is computed by a multi-scale matrix-based system reliability (MSR) method. Lower-scale MSR analyses evaluate the probabilities of the identified failure modes and their Statistical Dependence. A higher-scale MSR analysis evaluates the system failure probability based on the results of the lower-scale analyses. Three illustrative examples demonstrate the efficiency and accuracy of the approach through comparison with existing methods and Monte Carlo simulations. The results show that the proposed method skillfully identifies the dominant failure modes, including those neglected by existing approaches. The multi-scale MSR method accurately evaluates the system failure probability with Statistical Dependence fully considered. The decoupling between the failure mode identification and the system reliability evaluation allows for effective applications to larger structural systems.
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Single-loop system reliability-based topology optimization considering Statistical Dependence between limit-states
Structural and Multidisciplinary Optimization, 2011Co-Authors: Tam H. Nguyen, Junho Song, Glaucio H. PaulinoAbstract:This paper presents a single-loop algorithm for system reliability-based topology optimization (SRBTO) that can account for Statistical Dependence between multiple limit-states, and its applications to computationally demanding topology optimization (TO) problems. A single-loop reliability-based design optimization (RBDO) algorithm replaces the inner-loop iterations to evaluate probabilistic constraints by a non-iterative approximation. The proposed single-loop SRBTO algorithm accounts for the Statistical Dependence between the limit-states by using the matrix-based system reliability (MSR) method to compute the system failure probability and its parameter sensitivities. The SRBTO/MSR approach is applicable to general system events including series, parallel, cut-set and link-set systems and provides the gradients of the system failure probability to facilitate gradient-based optimization. In most RBTO applications, probabilistic constraints are evaluated by use of the first-order reliability method for efficiency. In order to improve the accuracy of the reliability calculations for RBDO or RBTO problems with high nonlinearity, we introduce a new single-loop RBDO scheme utilizing the second-order reliability method and implement it to the proposed SRBTO algorithm. Moreover, in order to overcome challenges in applying the proposed algorithm to computationally demanding topology optimization problems, we utilize the multiresolution topology optimization (MTOP) method, which achieves computational efficiency in topology optimization by assigning different levels of resolutions to three meshes representing finite element analysis, design variables and material density distribution respectively. The paper provides numerical examples of two- and three-dimensional topology optimization problems to demonstrate the proposed SRBTO algorithm and its applications. The optimal topologies from deterministic, component and system RBTOs are compared with one another to investigate the impact of optimization schemes on final topologies. Monte Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach.
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system reliability and sensitivity under Statistical Dependence by matrix based system reliability method
Structural Safety, 2009Co-Authors: Junho Song, Wonhee KangAbstract:Abstract A matrix-based system reliability (MSR) method has been recently proposed to compute the probabilities of general system events efficiently by simple matrix operations. The proposed matrix-based framework describes both a system event and the likelihood of its component events by vectors that are obtained by efficient matrix-based procedures. The probability of the system event is computed by the inner product of the two vectors. Therefore, the method is uniformly applicable to any type of system events including series, parallel, cut-set and link-set systems. In the case when one has incomplete information on component probabilities and/or on the Statistical Dependence between components, the matrix-based framework enables us to obtain the narrowest bounds on the system probability by linear programming. Various importance measures and conditional probabilities are also efficiently estimated by the proposed method. This paper presents the MSR method and further develops it in terms of Statistical Dependence and parameter sensitivity of system reliability. First, a method is developed to use the MSR method for systems with Statistically dependent components. The correlation coefficients between the basic random variables or the component safety margins are represented by a Dunnett–Sobel class correlation matrix to identify the source of the Statistical Dependence and to make use of the matrix-based procedure developed for independent components. Second, a new matrix-based procedure is proposed to calculate the sensitivities of system reliability with respect to parameters. This paper demonstrates the MSR method and these further developments by two numerical examples of structural systems. First, the system fragility of a bridge structure is computed based on the analytical fragility models of the bridge components and the correlation coefficients between the seismic demands at different components. In the second example, the MSR method is used to estimate the probability of the collapse of a statically indeterminate structure subjected to an abnormal load. The sensitivities of the probability with respect to the means and standard deviations of uncertain member capacities are estimated for an optimal upgrade of the structural system.
Shusaku Tsumoto - One of the best experts on this subject based on the ideXlab platform.
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WI-IAT (1) - Formal Analysis of Statistical Dependence Based Homological Algebra
2014 IEEE WIC ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:This paper proposes homological analysis of Statistical dependency graph. If a dependency graph model satisfy the condition of a chain complex, homological algebra can be applied. Especially, the degree of freedom can be viewed as a dual space of an original complex.
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degree of freedom and numbers of subdeterminants in contingency table
Granular Computing, 2012Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:This paper focuses on the degree of freedom and number of subdetermiants in a pearson residual in a multiway contingency table. The results show that multidimensional residuals are represented as linear sum of determinants of 2 × 2 submatrices, which can be viewed as information granules measuring the degree of Statistical Dependence. Furthermore, the number of subderminants in a residual is equal to the degree of freedom.
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Residual as Linear Sum of Matrix Determinants in Multiway Contingency Tables
International Journal of Computational Intelligence Systems, 2011Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:A Pearson residual is defined as a residual between an observed value and expected one of each cell in a contingency table, which measures the degree of Statistical Dependence of two attribute-value pairs corresponding to the cell. This paper shows that this residual is decomposed into a linear sum of determinants of 2 x 2 subtables, which means that the geometrical nature of the residuals can be viewed from grasmmanian algebra.
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information granules of Statistical Dependence in multiway contingency tables
Granular Computing, 2010Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:This paper focuses on residual analysis of Statistical inDependence of multiple variables from the viewpoint of linear algebra. The results show that multidimensional residuals are represented as linear sum of determinants of $2 \times 2$ submatrices, which can be viewed as information granules measuring the degree of Statistical Dependence.
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contingency matrix theory Statistical Dependence in a contingency table
Information Sciences, 2009Co-Authors: Shusaku TsumotoAbstract:Chance discovery aims at understanding the meaning of functional dependency from the viewpoint of unexpected relations. One of the most important observations is that such a chance is hidden under a huge number of coocurrencies extracted from a given data. On the other hand, conventional data-mining methods are strongly dependent on frequencies and statistics rather than interestingness or unexpectedness. This paper discusses some limitations of ideas of Statistical Dependence, especially focusing on the formal characteristics of Simpson's paradox from the viewpoint of linear algebra. Theoretical results show that such a Simpson's paradox can be observed when a given contingency table as a matrix is not regular, in other words, the rank of a contingency matrix is not full. Thus, data-ordered evidence gives some limitations, which should be compensated by human-oriented reasoning.
Baldassare Bacchi - One of the best experts on this subject based on the ideXlab platform.
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modelling the Statistical Dependence of rainfall event variables through copula functions
Hydrology and Earth System Sciences, 2011Co-Authors: Matteo Balistrocchi, Baldassare BacchiAbstract:Abstract. In many hydrological models, such as those derived by analytical probabilistic methods, the precipitation stochastic process is represented by means of individual storm random variables which are supposed to be independent of each other. However, several proposals were advanced to develop joint probability distributions able to account for the observed Statistical Dependence. The traditional technique of the multivariate statistics is nevertheless affected by several drawbacks, whose most evident issue is the unavoidable subordination of the Dependence structure assessment to the marginal distribution fitting. Conversely, the copula approach can overcome this limitation, by dividing the problem in two distinct parts. Furthermore, goodness-of-fit tests were recently made available and a significant improvement in the function selection reliability has been achieved. Herein the Dependence structure of the rainfall event volume, the wet weather duration and the interevent time is assessed and verified by test statistics with respect to three long time series recorded in different Italian climates. Paired analyses revealed a non negligible Dependence between volume and duration, while the interevent period proved to be substantially independent of the other variables. A unique copula model seems to be suitable for representing this Dependence structure, despite the sensitivity demonstrated by its parameter towards the threshold utilized in the procedure for extracting the independent events. The joint probability function was finally developed by adopting a Weibull model for the marginal distributions.
Andreas Schmitz - One of the best experts on this subject based on the ideXlab platform.
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measuring Statistical Dependence and coupling of subsystems
Physical Review E, 2000Co-Authors: Andreas SchmitzAbstract:We investigate recently proposed measures for the Statistical Dependence of systems with complex dynamical behavior. We consider appropriate model systems, to ensure that influences of individual properties of the systems are excluded. We demonstrate that it is indeed possible to obtain nontrivial directional information, but we also argue that the interpretation of this information is difficult.
Wonhee Kang - One of the best experts on this subject based on the ideXlab platform.
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system reliability and sensitivity under Statistical Dependence by matrix based system reliability method
Structural Safety, 2009Co-Authors: Junho Song, Wonhee KangAbstract:Abstract A matrix-based system reliability (MSR) method has been recently proposed to compute the probabilities of general system events efficiently by simple matrix operations. The proposed matrix-based framework describes both a system event and the likelihood of its component events by vectors that are obtained by efficient matrix-based procedures. The probability of the system event is computed by the inner product of the two vectors. Therefore, the method is uniformly applicable to any type of system events including series, parallel, cut-set and link-set systems. In the case when one has incomplete information on component probabilities and/or on the Statistical Dependence between components, the matrix-based framework enables us to obtain the narrowest bounds on the system probability by linear programming. Various importance measures and conditional probabilities are also efficiently estimated by the proposed method. This paper presents the MSR method and further develops it in terms of Statistical Dependence and parameter sensitivity of system reliability. First, a method is developed to use the MSR method for systems with Statistically dependent components. The correlation coefficients between the basic random variables or the component safety margins are represented by a Dunnett–Sobel class correlation matrix to identify the source of the Statistical Dependence and to make use of the matrix-based procedure developed for independent components. Second, a new matrix-based procedure is proposed to calculate the sensitivities of system reliability with respect to parameters. This paper demonstrates the MSR method and these further developments by two numerical examples of structural systems. First, the system fragility of a bridge structure is computed based on the analytical fragility models of the bridge components and the correlation coefficients between the seismic demands at different components. In the second example, the MSR method is used to estimate the probability of the collapse of a statically indeterminate structure subjected to an abnormal load. The sensitivities of the probability with respect to the means and standard deviations of uncertain member capacities are estimated for an optimal upgrade of the structural system.
